As the popularity of the Internet has increased, so has the prevalence of Web search engines. A Web search engine is a search engine designed to search for information on the World Wide Web (WWW). Information may consist of web pages, images, information and other types of files. Some search engines also mine data available in newsbooks, databases, or open directories. Unlike Web directories, which are maintained by human editors, search engines operate algorithmically or are a mixture of algorithmic and human input.
Web search engines work by indexing information about many web pages, which they retrieve from the WWW itself. When a user enters a query into a search engine (typically by using key words), the engine examines its index and provides a listing of best-matching web pages according to its criteria, usually with a short summary containing the document's title and sometimes parts of the text.
Some search engines generate metrics that measure internet activity related to particular search terms, topics, links, web pages, web sites, etc., which are sometimes used to allow users to discover the current most popular search terms, topics, links, web pages, web sites, etc. Some example search engine internet activity metrics and search engine features based on internet activity metrics include:
Ask IQ (http://about.ask.com/en/docs/iq/iq.shtml),
Dogpile SearchSpy (http://www.dogpile.com/info.dogpl/searchspy/),
MetaCrawler MetaSpy (http://www.metacrawler.com/),
Google Trends (http://google.com/trends),
Google Zeitgeist (http://www.google.com/intl/en/press/zeitgeist/index.html),
Lycos 50 (http://50.lycos.com/),
Yahoo Buzz Score,
dWoz Search Phrase Lists (http://www.dwoz.com/default.asp?Pr=122),
Google AdWords Keyword Tool                (https://adwords.google.com/select/KeywordToolExternal), and        
Search Term Research and Search Behavior                (http://searchenginewatch.com/showPage.html?page=_subscribers/topics)        
To illustrate one example, Yahoo!'s Buzz Score is a metric that helps identify the popularity of a search term by analyzing the search logs traffic. The metric is proportional to the number of searches for a given term and is normalized against the total number of searches on the network. Buzz Score can be segmented across categories of terms, age-gender segments and user locations.